1. Field of the Invention
The present invention pertains to auction designs and auction analysis. More particularly, this invention relates to an automated decision support system for designing and analyzing auctions.
2. Description of the Related Art
In an auction, the participants typically make a variety of decisions. In an auction run by a seller, a bidder has to decide on how to bid and whether or not to bid in a specific auction conditional on information the bidder has. In addition, the bidder needs to decide whether or not and how to gather information on auctions, objects, rivals, etc.
The auction house needs to decide fees for buyers and/or sellers. In addition, the auction house needs to decide the menu of auction mechanism to offer.
The seller has to make a number of decisions regarding the auction mechanism. This means that the seller typically makes a number of decisions to determine the specific auction procedure for designing and conducting the auction. The auction mechanism typically includes the auction format, the reserve price of the item to be auctioned, entry fees, quantity, timing and duration, lot size and bundling, sequence of lots, bid increments, information disclosure policy, participation rules, and preference/discrimination policy, etc. The auction format indicates the process by which the auction winner(s) and payments are determined. Standard auction formats include Dutch, English, first-price-sealed bid, Vickrey, etc.
The reserve price specifies the minimum or maximum acceptable price, depending on whether the auction is a selling auction or buying auction. If the auction is a selling auction, then the reserve price is the minimum acceptable price. If the auction is a buying auction, the reserve price indicates the maximum acceptable price. The entry fee is the fee a bidder is charged to participate in the auction. The participation rules specify how bidders can participate in the auction. For example, the participation rules can specify that the auction is an invitation only auction. As a further example, the participation rules may specify that participation is based on lottery draws.
For example, in an auction to sell an item (e.g., laser printer), the seller needs to determine, among other things, which auction format is to be employed in order to extract the maximum revenue from the auction. This is due to the fact that a particular auction format is better suited for a particular market environment than other auction formats. In other words, an English auction is better for some market environments, while in others, a first-price-sealed-bid auction is better. If a wrong auction format is selected by the seller, the revenue generated from the auction may be adversely affected.
In addition, once the seller has decided to employ a specific auction format, another important decision the seller typically needs to make is to set the reserve price below which no bid will be accepted. Again, the selection of the reserve price also affects the revenue generated from the auction. Estimates using field data from offshore oil lease auctions suggest that optimal reserve prices can increase auction revenues by more than 300%.
Currently, these decisions are left entirely to the person who runs the auction. In addition, uncertainty is intrinsic in all these decisions. This means that these decisions are person-dependent and typically optimality of these decisions cannot be ascertained. There is no systematic data analysis for the auction before the person can make the decisions. If the person making these decisions is experienced in auctions, then the decisions are likely to be optimal. If the person making these decisions is not experienced in auctions, then the decisions are likely not to be optimal.
Thus, there exists a need for an integrated estimation and optimization solution for making auction design decisions optimally based on structural econometric analysis of available data.